Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching

Minjeong Kim, Guorong Wu, Dinggang Shen

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows high sensitivity in detecting breast cancer. However, its performance could be affected by patient motion during the imaging. To overcome this problem, it is necessary to correct patient motion by deformable registration, before using the DCE-MRI to detect breast cancer. However, deformable registration of DCE-MR images is challenging due to the dramatic contrast change over time (especially between the precontrast and postcontrast images). Most existing methods typically register each postcontrast image onto the precontrast image independently, without considering the dynamic contrast change after agent uptake. This could lead to the inconsistency among the aligned postcontrast images in the precontrast image space, which will eventually result in worse performance in cancer detection. In this paper, the authors present a novel hierarchical registration framework to address this problem. Methods: First, the authors propose a hierarchical registration framework to deploy the groupwise registration for simultaneous registration of all postcontrast images onto their group-mean image and further aligning the group-mean image of postcontrast images onto the precontrast image space for final alignment of all precontrast and postcontrast images. In this way, the postcontrast images (with similar intensity patterns) can be jointly aligned onto the precontrast image for increasing their overall consistency after registration. Second, in order to improve the registration between the precontrast image and the group-mean image of the postcontrast images, the authors propose using the contrast-invariant attribute vectors to guide the robust feature matching during the registration. Results: Our proposed hierarchical registration framework has been comprehensively evaluated and compared with affine registration and widely used deformable registration methods in both pairwise and groupwise registration formulation. The experimental results on both real and simulated images show that our method can obtain not only more accurate but also more consistent registration results than any of all other registration algorithms.Conclusions: The authors have proposed a novel groupwise registration method to achieve accurate and consistent alignment for breast DCE-MR images. In the future, the authors will further evaluate our proposed method with more clinical datasets.

Original languageEnglish
Pages (from-to)353-366
Number of pages14
JournalMedical Physics
Volume39
Issue number1
DOIs
Publication statusPublished - 2012 Jan 1
Externally publishedYes

Fingerprint

Breast
Magnetic Resonance Imaging
Breast Neoplasms
Neoplasms

Keywords

  • breast tumor image
  • dynamic contrast-enhanced (DCE) MRI
  • feature-based deformable registration
  • groupwise registration
  • local steering kernel

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching. / Kim, Minjeong; Wu, Guorong; Shen, Dinggang.

In: Medical Physics, Vol. 39, No. 1, 01.01.2012, p. 353-366.

Research output: Contribution to journalArticle

@article{84e9d70b092946fab37e9c7151eb2d49,
title = "Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching",
abstract = "Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows high sensitivity in detecting breast cancer. However, its performance could be affected by patient motion during the imaging. To overcome this problem, it is necessary to correct patient motion by deformable registration, before using the DCE-MRI to detect breast cancer. However, deformable registration of DCE-MR images is challenging due to the dramatic contrast change over time (especially between the precontrast and postcontrast images). Most existing methods typically register each postcontrast image onto the precontrast image independently, without considering the dynamic contrast change after agent uptake. This could lead to the inconsistency among the aligned postcontrast images in the precontrast image space, which will eventually result in worse performance in cancer detection. In this paper, the authors present a novel hierarchical registration framework to address this problem. Methods: First, the authors propose a hierarchical registration framework to deploy the groupwise registration for simultaneous registration of all postcontrast images onto their group-mean image and further aligning the group-mean image of postcontrast images onto the precontrast image space for final alignment of all precontrast and postcontrast images. In this way, the postcontrast images (with similar intensity patterns) can be jointly aligned onto the precontrast image for increasing their overall consistency after registration. Second, in order to improve the registration between the precontrast image and the group-mean image of the postcontrast images, the authors propose using the contrast-invariant attribute vectors to guide the robust feature matching during the registration. Results: Our proposed hierarchical registration framework has been comprehensively evaluated and compared with affine registration and widely used deformable registration methods in both pairwise and groupwise registration formulation. The experimental results on both real and simulated images show that our method can obtain not only more accurate but also more consistent registration results than any of all other registration algorithms.Conclusions: The authors have proposed a novel groupwise registration method to achieve accurate and consistent alignment for breast DCE-MR images. In the future, the authors will further evaluate our proposed method with more clinical datasets.",
keywords = "breast tumor image, dynamic contrast-enhanced (DCE) MRI, feature-based deformable registration, groupwise registration, local steering kernel",
author = "Minjeong Kim and Guorong Wu and Dinggang Shen",
year = "2012",
month = "1",
day = "1",
doi = "10.1118/1.3665705",
language = "English",
volume = "39",
pages = "353--366",
journal = "Medical Physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "1",

}

TY - JOUR

T1 - Hierarchical alignment of breast DCE-MR images by groupwise registration and robust feature matching

AU - Kim, Minjeong

AU - Wu, Guorong

AU - Shen, Dinggang

PY - 2012/1/1

Y1 - 2012/1/1

N2 - Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows high sensitivity in detecting breast cancer. However, its performance could be affected by patient motion during the imaging. To overcome this problem, it is necessary to correct patient motion by deformable registration, before using the DCE-MRI to detect breast cancer. However, deformable registration of DCE-MR images is challenging due to the dramatic contrast change over time (especially between the precontrast and postcontrast images). Most existing methods typically register each postcontrast image onto the precontrast image independently, without considering the dynamic contrast change after agent uptake. This could lead to the inconsistency among the aligned postcontrast images in the precontrast image space, which will eventually result in worse performance in cancer detection. In this paper, the authors present a novel hierarchical registration framework to address this problem. Methods: First, the authors propose a hierarchical registration framework to deploy the groupwise registration for simultaneous registration of all postcontrast images onto their group-mean image and further aligning the group-mean image of postcontrast images onto the precontrast image space for final alignment of all precontrast and postcontrast images. In this way, the postcontrast images (with similar intensity patterns) can be jointly aligned onto the precontrast image for increasing their overall consistency after registration. Second, in order to improve the registration between the precontrast image and the group-mean image of the postcontrast images, the authors propose using the contrast-invariant attribute vectors to guide the robust feature matching during the registration. Results: Our proposed hierarchical registration framework has been comprehensively evaluated and compared with affine registration and widely used deformable registration methods in both pairwise and groupwise registration formulation. The experimental results on both real and simulated images show that our method can obtain not only more accurate but also more consistent registration results than any of all other registration algorithms.Conclusions: The authors have proposed a novel groupwise registration method to achieve accurate and consistent alignment for breast DCE-MR images. In the future, the authors will further evaluate our proposed method with more clinical datasets.

AB - Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows high sensitivity in detecting breast cancer. However, its performance could be affected by patient motion during the imaging. To overcome this problem, it is necessary to correct patient motion by deformable registration, before using the DCE-MRI to detect breast cancer. However, deformable registration of DCE-MR images is challenging due to the dramatic contrast change over time (especially between the precontrast and postcontrast images). Most existing methods typically register each postcontrast image onto the precontrast image independently, without considering the dynamic contrast change after agent uptake. This could lead to the inconsistency among the aligned postcontrast images in the precontrast image space, which will eventually result in worse performance in cancer detection. In this paper, the authors present a novel hierarchical registration framework to address this problem. Methods: First, the authors propose a hierarchical registration framework to deploy the groupwise registration for simultaneous registration of all postcontrast images onto their group-mean image and further aligning the group-mean image of postcontrast images onto the precontrast image space for final alignment of all precontrast and postcontrast images. In this way, the postcontrast images (with similar intensity patterns) can be jointly aligned onto the precontrast image for increasing their overall consistency after registration. Second, in order to improve the registration between the precontrast image and the group-mean image of the postcontrast images, the authors propose using the contrast-invariant attribute vectors to guide the robust feature matching during the registration. Results: Our proposed hierarchical registration framework has been comprehensively evaluated and compared with affine registration and widely used deformable registration methods in both pairwise and groupwise registration formulation. The experimental results on both real and simulated images show that our method can obtain not only more accurate but also more consistent registration results than any of all other registration algorithms.Conclusions: The authors have proposed a novel groupwise registration method to achieve accurate and consistent alignment for breast DCE-MR images. In the future, the authors will further evaluate our proposed method with more clinical datasets.

KW - breast tumor image

KW - dynamic contrast-enhanced (DCE) MRI

KW - feature-based deformable registration

KW - groupwise registration

KW - local steering kernel

UR - http://www.scopus.com/inward/record.url?scp=84855467948&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84855467948&partnerID=8YFLogxK

U2 - 10.1118/1.3665705

DO - 10.1118/1.3665705

M3 - Article

C2 - 22225305

AN - SCOPUS:84855467948

VL - 39

SP - 353

EP - 366

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 1

ER -